3D Printed Microneedles for the Transdermal Delivery of NAD<sup>+</sup> Precursor: Toward Personalization of Skin Delivery
Bibliographic record
Abstract
3D printing of microneedles (μNDs) for transdermal therapy has the potential to enable patient personalization based on the target disease, site of application, and dosage requirements. To convert this concept to reality, it is necessary that the 3D printing technology can deliver high resolution, an affordable cost, and large print volumes. With the introduction of benchtop 4K and 8K 3D printers, it is now possible to manufacture medical devices like μNDs at sufficient resolution and low cost. In this research, we systematically optimized the 3D printing design parameters such as resin viscosity, print angle, layer height, and curing time to generate customizable μNDs. We have also developed an innovative 3D coating microtank device to optimize the coating method. We have applied this to the development of novel μNDs to deliver an established NAD + precursor molecule, nicotinamide mononucleotide (NMN). A methacrylate-based polymer photoresin (eSun resin) was diluted with methanol to adjust the resin viscosity. The 3D print layer height of 25 μm yielded a smooth surface, thus reducing edge-ridge mismatches. Printing μNDs at 90° to the print platform yielded 84.28 ± 2.158% ( n = 5) of the input height thus increasing the tip sharpness (48.52 ± 10.43 μm, n = 5). The formulation containing fluorescein (model molecule), sucrose (viscosity modifier), and Tween-20 (surface tension modifier) was coated on the μNDs using the custom designed microtank setup, and the amount deposited was determined fluorescently. The dye-coated μND arrays inserted into human skin ( in vitro ) showed a fluorescence signal at a depth of 150 μm ( n = 3) into the skin. After optimization of the 3D printing parameters and coating protocol using fluorescein, NMN was coated onto the μNDs, and its diffusion was assessed in full-thickness human skin in vitro using a Franz diffusion setup. Approximately 189 ± 34.5 μg (5× dipped coated μNDs) of NMN permeated through the skin and 41.2 ± 7.53 μg was left in the skin after 24 h. Multiphoton microscopy imaging of NMN-coated μND treated mouse ear skin ex vivo demonstrated significantly ( p < 0.05) increased free-unbound NADPH and reduced fluorescence lifetime of NADPH, both of which are indicative of cellular metabolic rates. Our study demonstrates that low-cost benchtop 3D printers can be used to print high-fidelity μNDs with the ability to rapidly coat and release NMN which consequently caused changes in intracellular NAD + levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".